Arabic Document Image Classification Using Neural Networks.

Authors

Abdallah Al-Khorabi, Sana'a University, Sana'a, Yemen Republic P. 0. Box 1341, Fax 967-1-2505 14Follow
Mohamed Mansour, Postgraduate Student University of Science and Technology Sana'a, Yemen Republic P, 0, Box 1341Follow

Corresponding Author

Al-Khorabi, Abdallah

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

The Neural Network Arabic Document Image Classification System (NNADICS) is an adaptive Arabic document classifier. By training NNADICS on a number of different document image types, NNADICS behaves as a multiple classifier, since it is capable for distinguishing between multiple document image types. NNADICS is designed, built, tested and evaluated. After training NNADICS a document image is applied to the system for classification. Before that the document image is scanned, pre-processed and binarized, and then applied to NNADICS to classify its contents to text, geometric, or photographic image type. NNADICS achieved an average of a 86% recognition rate as it is clearly demonstrated.

Recommended Citation

Al-Khorabi, Abdallah and Mansour, Mohamed (2020) "Arabic Document Image Classification Using Neural Networks.," Mansoura Engineering Journal: Vol. 29 : Iss. 1 , Article 18.
Available at: https://doi.org/10.21608/bfemu.2020.132761